Overview

Dataset statistics

Number of variables49
Number of observations49847
Missing cells41116
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.3 MiB
Average record size in memory2.3 KiB

Variable types

Numeric12
Categorical23
Boolean14

Alerts

ANALYTES has constant value "B" Constant
HOUSE_NUM has a high cardinality: 1571 distinct values High cardinality
STREET_NAME has a high cardinality: 4107 distinct values High cardinality
CONFIRMATION_NUM has a high cardinality: 41010 distinct values High cardinality
INSPECTION_BY_FIRM has a high cardinality: 700 distinct values High cardinality
INSPECTION_DATE has a high cardinality: 3116 distinct values High cardinality
LAB_NAME has a high cardinality: 738 distinct values High cardinality
NTA has a high cardinality: 147 distinct values High cardinality
BIN is highly correlated with ZIP and 3 other fieldsHigh correlation
ZIP is highly correlated with BOROUGH and 6 other fieldsHigh correlation
BLOCK is highly correlated with BOROUGH and 7 other fieldsHigh correlation
LOT is highly correlated with ANALYTESHigh correlation
REPORTING_YEAR is highly correlated with BATCH_DATEHigh correlation
TANK_NUM is highly correlated with ANALYTESHigh correlation
LATITUDE is highly correlated with BOROUGH and 7 other fieldsHigh correlation
LONGITUDE is highly correlated with BOROUGH and 7 other fieldsHigh correlation
COMMUNITY_BOARD is highly correlated with BLOCK and 3 other fieldsHigh correlation
COUNCIL_DISTRICT is highly correlated with BOROUGH and 7 other fieldsHigh correlation
CENSUS_TRACT is highly correlated with ZIP and 4 other fieldsHigh correlation
BBL is highly correlated with BOROUGH and 8 other fieldsHigh correlation
BOROUGH is highly correlated with ZIP and 5 other fieldsHigh correlation
INSPECTION_PERFORMED is highly correlated with GI_REQ_INTERNAL_STRUCTURE and 9 other fieldsHigh correlation
GI_REQ_INTERNAL_STRUCTURE is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
GI_RESULT_INTERNAL_STRUCTURE is highly correlated with GI_RESULT_EXTERNAL_STRUCTURE and 3 other fieldsHigh correlation
GI_REQ_EXTERNAL_STRUCTURE is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
GI_RESULT_EXTERNAL_STRUCTURE is highly correlated with GI_RESULT_INTERNAL_STRUCTURE and 5 other fieldsHigh correlation
GI_REQ_OVERFLOW_PIPES is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
GI_RESULT_OVERFLOW_PIPES is highly correlated with GI_RESULT_EXTERNAL_STRUCTURE and 2 other fieldsHigh correlation
GI_REQ_ACCESS_LADDERS is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
GI_RESULT_ACCESS_LADDERS is highly correlated with GI_RESULT_INTERNAL_STRUCTURE and 4 other fieldsHigh correlation
GI_REQ_AIR_VENTS is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
GI_RESULT_AIR_VENTS is highly correlated with GI_RESULT_INTERNAL_STRUCTURE and 3 other fieldsHigh correlation
GI_REQ_ROOF_ACCESS is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
GI_RESULT_ROOF_ACCESS is highly correlated with GI_RESULT_EXTERNAL_STRUCTURE and 1 other fieldsHigh correlation
SI_REQ_SEDIMENT is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
SI_RESULT_SEDIMENT is highly correlated with SI_RESULT_DEBRIS_INSECTSHigh correlation
SI_REQ_BIOLOGICAL_GROWTH is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
SI_RESULT_BIOLOGICAL_GROWTH is highly correlated with SI_RESULT_DEBRIS_INSECTS and 1 other fieldsHigh correlation
SI_REQ_DEBRIS_INSECTS is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
SI_RESULT_DEBRIS_INSECTS is highly correlated with SI_RESULT_SEDIMENT and 1 other fieldsHigh correlation
SI_REQ_RODENT_BIRD is highly correlated with INSPECTION_PERFORMED and 9 other fieldsHigh correlation
SI_RESULT_RODENT_BIRD is highly correlated with SI_RESULT_BIOLOGICAL_GROWTH and 1 other fieldsHigh correlation
SAMPLE_COLLECTED is highly correlated with ECOLI and 4 other fieldsHigh correlation
NYS_CERTIFIED is highly correlated with MEET_STANDARDSHigh correlation
ANALYTES is highly correlated with ECOLI and 28 other fieldsHigh correlation
COLIFORM is highly correlated with ECOLIHigh correlation
ECOLI is highly correlated with COLIFORM and 1 other fieldsHigh correlation
MEET_STANDARDS is highly correlated with GI_RESULT_INTERNAL_STRUCTURE and 5 other fieldsHigh correlation
DELETED is highly correlated with MEET_STANDARDS and 3 other fieldsHigh correlation
BATCH_DATE is highly correlated with REPORTING_YEARHigh correlation
MEET_STANDARDS has 38723 (77.7%) missing values Missing
BIN is highly skewed (γ1 = 32.81932176) Skewed
CONFIRMATION_NUM is uniformly distributed Uniform

Reproduction

Analysis started2024-05-07 19:20:48.568146
Analysis finished2024-05-07 19:21:48.177178
Duration59.61 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

BIN
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct5559
Distinct (%)11.2%
Missing112
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4251135.238
Minimum1000000
Maximum4050200001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:48.271053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1000000
5-th percentile1002348
Q11020194
median1041701
Q31082759
95-th percentile3348849
Maximum4050200001
Range4049200001
Interquartile range (IQR)62565

Descriptive statistics

Standard deviation66882052.86
Coefficient of variation (CV)15.73275116
Kurtosis1433.246056
Mean4251135.238
Median Absolute Deviation (MAD)26118
Skewness32.81932176
Sum2.114302111 × 1011
Variance4.473208994 × 1015
MonotonicityNot monotonic
2024-05-07T15:21:48.391298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1084781115
 
0.2%
1083346100
 
0.2%
107141490
 
0.2%
102631880
 
0.2%
100139479
 
0.2%
101586271
 
0.1%
100122770
 
0.1%
100102569
 
0.1%
100000068
 
0.1%
108573559
 
0.1%
Other values (5549)48934
98.2%
(Missing)112
 
0.2%
ValueCountFrequency (%)
100000068
0.1%
100000532
0.1%
100000622
 
< 0.1%
100000716
 
< 0.1%
10000189
 
< 0.1%
100002019
 
< 0.1%
100002130
0.1%
10000239
 
< 0.1%
100002521
 
< 0.1%
10000278
 
< 0.1%
ValueCountFrequency (%)
40502000011
< 0.1%
40208600401
< 0.1%
40026800011
< 0.1%
30650800061
< 0.1%
30362100011
< 0.1%
30190200011
< 0.1%
30128300011
< 0.1%
30107200401
< 0.1%
30012200101
< 0.1%
20434700071
< 0.1%

BOROUGH
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
MANHATTAN
42593 
BROOKLYN
 
2925
BRONX
 
2291
QUEENS
 
1962
STATEN ISLAND
 
76

Length

Max length13
Median length9
Mean length8.645495215
Min length5

Characters and Unicode

Total characters430952
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANHATTAN
2nd rowMANHATTAN
3rd rowBRONX
4th rowMANHATTAN
5th rowQUEENS

Common Values

ValueCountFrequency (%)
MANHATTAN42593
85.4%
BROOKLYN2925
 
5.9%
BRONX2291
 
4.6%
QUEENS1962
 
3.9%
STATEN ISLAND76
 
0.2%

Length

2024-05-07T15:21:48.580975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:48.749991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manhattan42593
85.3%
brooklyn2925
 
5.9%
bronx2291
 
4.6%
queens1962
 
3.9%
staten76
 
0.2%
island76
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A127931
29.7%
N92516
21.5%
T85338
19.8%
M42593
 
9.9%
H42593
 
9.9%
O8141
 
1.9%
B5216
 
1.2%
R5216
 
1.2%
E4000
 
0.9%
L3001
 
0.7%
Other values (9)14407
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter430876
> 99.9%
Space Separator76
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A127931
29.7%
N92516
21.5%
T85338
19.8%
M42593
 
9.9%
H42593
 
9.9%
O8141
 
1.9%
B5216
 
1.2%
R5216
 
1.2%
E4000
 
0.9%
L3001
 
0.7%
Other values (8)14331
 
3.3%
Space Separator
ValueCountFrequency (%)
76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin430876
> 99.9%
Common76
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A127931
29.7%
N92516
21.5%
T85338
19.8%
M42593
 
9.9%
H42593
 
9.9%
O8141
 
1.9%
B5216
 
1.2%
R5216
 
1.2%
E4000
 
0.9%
L3001
 
0.7%
Other values (8)14331
 
3.3%
Common
ValueCountFrequency (%)
76
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII430952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A127931
29.7%
N92516
21.5%
T85338
19.8%
M42593
 
9.9%
H42593
 
9.9%
O8141
 
1.9%
B5216
 
1.2%
R5216
 
1.2%
E4000
 
0.9%
L3001
 
0.7%
Other values (9)14407
 
3.3%

ZIP
Real number (ℝ≥0)

HIGH CORRELATION

Distinct177
Distinct (%)0.4%
Missing71
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean10170.86674
Minimum10001
Maximum11694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:48.891883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10002
Q110016
median10022
Q310065
95-th percentile11217
Maximum11694
Range1693
Interquartile range (IQR)49

Descriptive statistics

Standard deviation373.8736833
Coefficient of variation (CV)0.03675927457
Kurtosis4.615142951
Mean10170.86674
Median Absolute Deviation (MAD)11
Skewness2.479114903
Sum506265063
Variance139781.531
MonotonicityNot monotonic
2024-05-07T15:21:49.022754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100222919
 
5.9%
100162713
 
5.4%
100192237
 
4.5%
100012147
 
4.3%
100171994
 
4.0%
100241971
 
4.0%
100031911
 
3.8%
100361888
 
3.8%
100251863
 
3.7%
100231834
 
3.7%
Other values (167)28299
56.8%
ValueCountFrequency (%)
100012147
4.3%
10002411
 
0.8%
100031911
3.8%
10004589
 
1.2%
10005517
 
1.0%
10006279
 
0.6%
10007746
 
1.5%
10009174
 
0.3%
100101453
2.9%
100111623
3.3%
ValueCountFrequency (%)
1169426
 
0.1%
1169223
 
< 0.1%
1169155
 
0.1%
1143520
 
< 0.1%
1143436
 
0.1%
1143313
 
< 0.1%
11432175
0.4%
114216
 
< 0.1%
1141825
 
0.1%
1141553
 
0.1%

HOUSE_NUM
Categorical

HIGH CARDINALITY

Distinct1571
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
1
 
791
200
 
666
10
 
515
300
 
437
50
 
420
Other values (1566)
47018 

Length

Max length9
Median length3
Mean length2.918951993
Min length1

Characters and Unicode

Total characters145501
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)0.2%

Sample

1st row405
2nd row131
3rd row1118
4th row28
5th row30-30

Common Values

ValueCountFrequency (%)
1791
 
1.6%
200666
 
1.3%
10515
 
1.0%
300437
 
0.9%
50420
 
0.8%
40409
 
0.8%
55389
 
0.8%
150383
 
0.8%
2372
 
0.7%
30369
 
0.7%
Other values (1561)45096
90.5%

Length

2024-05-07T15:21:49.249178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1791
 
1.6%
200666
 
1.3%
10515
 
1.0%
300437
 
0.9%
50420
 
0.8%
40409
 
0.8%
55389
 
0.8%
150383
 
0.8%
2372
 
0.7%
30369
 
0.7%
Other values (1558)45096
90.5%

Most occurring characters

ValueCountFrequency (%)
126537
18.2%
023184
15.9%
519414
13.3%
218048
12.4%
315185
10.4%
412770
8.8%
68462
 
5.8%
77400
 
5.1%
96065
 
4.2%
86024
 
4.1%
Other values (19)2412
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number143089
98.3%
Dash Punctuation2282
 
1.6%
Lowercase Letter72
 
< 0.1%
Uppercase Letter58
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
126537
18.5%
023184
16.2%
519414
13.6%
218048
12.6%
315185
10.6%
412770
8.9%
68462
 
5.9%
77400
 
5.2%
96065
 
4.2%
86024
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
e21
29.2%
n12
16.7%
a9
12.5%
s9
12.5%
t9
12.5%
w7
 
9.7%
i2
 
2.8%
r2
 
2.8%
b1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
A16
27.6%
O13
22.4%
S10
17.2%
B5
 
8.6%
D4
 
6.9%
P3
 
5.2%
E3
 
5.2%
W3
 
5.2%
N1
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
-2282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common145371
99.9%
Latin130
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e21
16.2%
A16
12.3%
O13
10.0%
n12
9.2%
S10
7.7%
a9
6.9%
s9
6.9%
t9
6.9%
w7
 
5.4%
B5
 
3.8%
Other values (8)19
14.6%
Common
ValueCountFrequency (%)
126537
18.3%
023184
15.9%
519414
13.4%
218048
12.4%
315185
10.4%
412770
8.8%
68462
 
5.8%
77400
 
5.1%
96065
 
4.2%
86024
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII145501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
126537
18.2%
023184
15.9%
519414
13.3%
218048
12.4%
315185
10.4%
412770
8.8%
68462
 
5.8%
77400
 
5.1%
96065
 
4.2%
86024
 
4.1%
Other values (19)2412
 
1.7%

STREET_NAME
Categorical

HIGH CARDINALITY

Distinct4107
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Broadway
 
1389
PARK AVENUE
 
942
Park Avenue
 
905
Fifth Avenue
 
866
BROADWAY
 
730
Other values (4102)
45015 

Length

Max length28
Median length26
Mean length13.76594379
Min length4

Characters and Unicode

Total characters686191
Distinct characters68
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1057 ?
Unique (%)2.1%

Sample

1st rowLEXINGTON AVE
2nd rowEast 66th Street
3rd rowgrand concourse
4th rowEAST 10 STREET
5th row47th Avenue

Common Values

ValueCountFrequency (%)
Broadway1389
 
2.8%
PARK AVENUE942
 
1.9%
Park Avenue905
 
1.8%
Fifth Avenue866
 
1.7%
BROADWAY730
 
1.5%
Madison Avenue532
 
1.1%
WEST END AVENUE471
 
0.9%
West End Avenue471
 
0.9%
5 AVENUE454
 
0.9%
FIFTH AVENUE379
 
0.8%
Other values (4097)42708
85.7%

Length

2024-05-07T15:21:49.404492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street24782
20.0%
avenue12199
 
9.8%
west12082
 
9.8%
east10421
 
8.4%
park3389
 
2.7%
ave2356
 
1.9%
st2288
 
1.8%
broadway2179
 
1.8%
fifth1549
 
1.3%
end1310
 
1.1%
Other values (1136)51297
41.4%

Most occurring characters

ValueCountFrequency (%)
83157
 
12.1%
e61299
 
8.9%
t58805
 
8.6%
E57077
 
8.3%
S41256
 
6.0%
T37915
 
5.5%
A27948
 
4.1%
r26045
 
3.8%
R19477
 
2.8%
a19317
 
2.8%
Other values (58)253895
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter279606
40.7%
Uppercase Letter273957
39.9%
Space Separator83157
 
12.1%
Decimal Number49129
 
7.2%
Other Punctuation331
 
< 0.1%
Dash Punctuation11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e61299
21.9%
t58805
21.0%
r26045
9.3%
a19317
 
6.9%
s18538
 
6.6%
n16142
 
5.8%
h12990
 
4.6%
v10630
 
3.8%
u9599
 
3.4%
d8406
 
3.0%
Other values (16)37835
13.5%
Uppercase Letter
ValueCountFrequency (%)
E57077
20.8%
S41256
15.1%
T37915
13.8%
A27948
10.2%
R19477
 
7.1%
W15030
 
5.5%
N10687
 
3.9%
V7658
 
2.8%
U7241
 
2.6%
P5984
 
2.2%
Other values (16)43684
15.9%
Decimal Number
ValueCountFrequency (%)
56237
12.7%
76134
12.5%
15549
11.3%
35515
11.2%
25345
10.9%
65043
10.3%
44942
10.1%
84495
9.1%
93440
7.0%
02429
 
4.9%
Other Punctuation
ValueCountFrequency (%)
.324
97.9%
,4
 
1.2%
\2
 
0.6%
'1
 
0.3%
Space Separator
ValueCountFrequency (%)
83157
100.0%
Dash Punctuation
ValueCountFrequency (%)
-11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin553563
80.7%
Common132628
 
19.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e61299
 
11.1%
t58805
 
10.6%
E57077
 
10.3%
S41256
 
7.5%
T37915
 
6.8%
A27948
 
5.0%
r26045
 
4.7%
R19477
 
3.5%
a19317
 
3.5%
s18538
 
3.3%
Other values (42)185886
33.6%
Common
ValueCountFrequency (%)
83157
62.7%
56237
 
4.7%
76134
 
4.6%
15549
 
4.2%
35515
 
4.2%
25345
 
4.0%
65043
 
3.8%
44942
 
3.7%
84495
 
3.4%
93440
 
2.6%
Other values (6)2771
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII686191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83157
 
12.1%
e61299
 
8.9%
t58805
 
8.6%
E57077
 
8.3%
S41256
 
6.0%
T37915
 
5.5%
A27948
 
4.1%
r26045
 
3.8%
R19477
 
2.8%
a19317
 
2.8%
Other values (58)253895
37.0%

BLOCK
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1750
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1421.629266
Minimum0
Maximum20008
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:49.505723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76
Q1825
median1215
Q31483
95-th percentile3943
Maximum20008
Range20008
Interquartile range (IQR)658

Descriptive statistics

Standard deviation1478.282916
Coefficient of variation (CV)1.039851213
Kurtosis28.86119515
Mean1421.629266
Median Absolute Deviation (MAD)352
Skewness4.454009872
Sum70863954
Variance2185320.38
MonotonicityNot monotonic
2024-05-07T15:21:49.621931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16328
 
0.7%
1293169
 
0.3%
838166
 
0.3%
1261164
 
0.3%
1263157
 
0.3%
1171142
 
0.3%
1290142
 
0.3%
1289136
 
0.3%
1480136
 
0.3%
833132
 
0.3%
Other values (1740)48175
96.6%
ValueCountFrequency (%)
010
 
< 0.1%
111
 
< 0.1%
28
 
< 0.1%
432
0.1%
538
0.1%
626
0.1%
84
 
< 0.1%
958
0.1%
1044
0.1%
1140
0.1%
ValueCountFrequency (%)
200081
 
< 0.1%
1617726
0.1%
160839
 
< 0.1%
1600114
 
< 0.1%
1595518
< 0.1%
1581017
< 0.1%
1563813
 
< 0.1%
156105
 
< 0.1%
155372
 
< 0.1%
1249536
0.1%

LOT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct235
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1342.518085
Minimum0
Maximum9999
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:49.773124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median31
Q367
95-th percentile7502
Maximum9999
Range9999
Interquartile range (IQR)57

Descriptive statistics

Standard deviation2847.860064
Coefficient of variation (CV)2.121282458
Kurtosis0.9580028125
Mean1342.518085
Median Absolute Deviation (MAD)26
Skewness1.716657115
Sum66920499
Variance8110306.945
MonotonicityNot monotonic
2024-05-07T15:21:49.889153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18150
 
16.4%
75015104
 
10.2%
75022031
 
4.1%
291004
 
2.0%
33851
 
1.7%
20847
 
1.7%
7503801
 
1.6%
21774
 
1.6%
5774
 
1.6%
23742
 
1.5%
Other values (225)28769
57.7%
ValueCountFrequency (%)
010
 
< 0.1%
18150
16.4%
2415
 
0.8%
3425
 
0.9%
4351
 
0.7%
5774
 
1.6%
6471
 
0.9%
7606
 
1.2%
8547
 
1.1%
9490
 
1.0%
ValueCountFrequency (%)
99991
 
< 0.1%
910010
< 0.1%
90806
< 0.1%
90789
< 0.1%
90622
 
< 0.1%
90598
< 0.1%
90293
 
< 0.1%
90248
< 0.1%
90217
< 0.1%
90209
< 0.1%

CONFIRMATION_NUM
Categorical

HIGH CARDINALITY
UNIFORM

Distinct41010
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
WTI4549961683
 
13
WTI2501915939
 
13
WTI0972981393
 
13
WTI4421642046
 
13
WTI3032892368
 
13
Other values (41005)
49782 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters648011
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35150 ?
Unique (%)70.5%

Sample

1st rowWTI0794137521
2nd rowWTI9605598636
3rd rowWTI2761355464
4th rowWTI5921313699
5th rowWTI5300059568

Common Values

ValueCountFrequency (%)
WTI454996168313
 
< 0.1%
WTI250191593913
 
< 0.1%
WTI097298139313
 
< 0.1%
WTI442164204613
 
< 0.1%
WTI303289236813
 
< 0.1%
WTI880001201313
 
< 0.1%
WTI094997283013
 
< 0.1%
WTI851384593313
 
< 0.1%
WTI031268790013
 
< 0.1%
WTI378598349013
 
< 0.1%
Other values (41000)49717
99.7%

Length

2024-05-07T15:21:50.025797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wti454996168313
 
< 0.1%
wti851384593313
 
< 0.1%
wti250191593913
 
< 0.1%
wti933238125613
 
< 0.1%
wti378598349013
 
< 0.1%
wti031268790013
 
< 0.1%
wti150395852613
 
< 0.1%
wti094997283013
 
< 0.1%
wti880001201313
 
< 0.1%
wti303289236813
 
< 0.1%
Other values (41000)49717
99.7%

Most occurring characters

ValueCountFrequency (%)
350262
 
7.8%
950196
 
7.7%
650140
 
7.7%
249993
 
7.7%
849878
 
7.7%
449875
 
7.7%
W49847
 
7.7%
T49847
 
7.7%
I49847
 
7.7%
149805
 
7.7%
Other values (3)148321
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number498470
76.9%
Uppercase Letter149541
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
350262
10.1%
950196
10.1%
650140
10.1%
249993
10.0%
849878
10.0%
449875
10.0%
149805
10.0%
749622
10.0%
549433
9.9%
049266
9.9%
Uppercase Letter
ValueCountFrequency (%)
W49847
33.3%
T49847
33.3%
I49847
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common498470
76.9%
Latin149541
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
350262
10.1%
950196
10.1%
650140
10.1%
249993
10.0%
849878
10.0%
449875
10.0%
149805
10.0%
749622
10.0%
549433
9.9%
049266
9.9%
Latin
ValueCountFrequency (%)
W49847
33.3%
T49847
33.3%
I49847
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII648011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
350262
 
7.8%
950196
 
7.7%
650140
 
7.7%
249993
 
7.7%
849878
 
7.7%
449875
 
7.7%
W49847
 
7.7%
T49847
 
7.7%
I49847
 
7.7%
149805
 
7.7%
Other values (3)148321
22.9%

REPORTING_YEAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.294321
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:50.144111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2015
Q12017
median2019
Q32021
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.546163373
Coefficient of variation (CV)0.001260917414
Kurtosis-0.9346836283
Mean2019.294321
Median Absolute Deviation (MAD)2
Skewness-0.1619628901
Sum100655764
Variance6.482947923
MonotonicityNot monotonic
2024-05-07T15:21:50.255667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20206503
13.0%
20236012
12.1%
20225974
12.0%
20215887
11.8%
20195804
11.6%
20185541
11.1%
20174683
9.4%
20154584
9.2%
20164545
9.1%
2024292
 
0.6%
Other values (3)22
 
< 0.1%
ValueCountFrequency (%)
20003
 
< 0.1%
20112
 
< 0.1%
201417
 
< 0.1%
20154584
9.2%
20164545
9.1%
20174683
9.4%
20185541
11.1%
20195804
11.6%
20206503
13.0%
20215887
11.8%
ValueCountFrequency (%)
2024292
 
0.6%
20236012
12.1%
20225974
12.0%
20215887
11.8%
20206503
13.0%
20195804
11.6%
20185541
11.1%
20174683
9.4%
20164545
9.1%
20154584
9.2%

TANK_NUM
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.304371376
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:50.352412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8898090643
Coefficient of variation (CV)0.6821746324
Kurtosis37.62938186
Mean1.304371376
Median Absolute Deviation (MAD)0
Skewness5.14079338
Sum65019
Variance0.7917601709
MonotonicityNot monotonic
2024-05-07T15:21:50.451292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
141010
82.3%
25860
 
11.8%
31528
 
3.1%
4720
 
1.4%
5267
 
0.5%
6178
 
0.4%
7104
 
0.2%
870
 
0.1%
941
 
0.1%
1031
 
0.1%
Other values (3)38
 
0.1%
ValueCountFrequency (%)
141010
82.3%
25860
 
11.8%
31528
 
3.1%
4720
 
1.4%
5267
 
0.5%
6178
 
0.4%
7104
 
0.2%
870
 
0.1%
941
 
0.1%
1031
 
0.1%
ValueCountFrequency (%)
1312
 
< 0.1%
1213
 
< 0.1%
1113
 
< 0.1%
1031
 
0.1%
941
 
0.1%
870
 
0.1%
7104
 
0.2%
6178
 
0.4%
5267
 
0.5%
4720
1.4%

INSPECTION_BY_FIRM
Categorical

HIGH CARDINALITY

Distinct700
Distinct (%)1.4%
Missing22
Missing (%)< 0.1%
Memory size3.6 MiB
Rosenwach Tank Co. LLC
21887 
ISSEKS BROS INC
8139 
American Pipe and Tank
4010 
Isseks Bros. Inc.
 
1764
isseks bros., inc.
 
1246
Other values (695)
12779 

Length

Max length69
Median length22
Mean length19.09890617
Min length1

Characters and Unicode

Total characters951603
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)0.6%

Sample

1st rowISSEKS BROS INC
2nd rowRosenwach Tank Co. LLC
3rd rowDCAS
4th rowISSEKS BROS INC
5th rowRosenwach Tank Co. LLC

Common Values

ValueCountFrequency (%)
Rosenwach Tank Co. LLC21887
43.9%
ISSEKS BROS INC8139
 
16.3%
American Pipe and Tank4010
 
8.0%
Isseks Bros. Inc.1764
 
3.5%
isseks bros., inc.1246
 
2.5%
ISSEKS BROS., INC.1028
 
2.1%
Isseks Bros Inc935
 
1.9%
Isseks834
 
1.7%
Atlantank, LLC742
 
1.5%
Nalco725
 
1.5%
Other values (690)8515
 
17.1%

Length

2024-05-07T15:21:50.568124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tank28052
16.3%
llc22969
13.4%
co22163
12.9%
rosenwach22002
12.8%
isseks15781
9.2%
inc15651
9.1%
bros14673
8.5%
american5962
 
3.5%
pipe5946
 
3.5%
and4524
 
2.6%
Other values (375)14179
8.2%

Most occurring characters

ValueCountFrequency (%)
122149
 
12.8%
n73678
 
7.7%
a68481
 
7.2%
C57938
 
6.1%
o54592
 
5.7%
s47002
 
4.9%
L46723
 
4.9%
e42537
 
4.5%
S39039
 
4.1%
c35610
 
3.7%
Other values (59)363854
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter470220
49.4%
Uppercase Letter319291
33.6%
Space Separator122149
 
12.8%
Other Punctuation39861
 
4.2%
Decimal Number57
 
< 0.1%
Math Symbol16
 
< 0.1%
Dash Punctuation8
 
< 0.1%
Open Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C57938
18.1%
L46723
14.6%
S39039
12.2%
R32611
10.2%
I30948
9.7%
T28855
9.0%
B14013
 
4.4%
N13504
 
4.2%
E11531
 
3.6%
K10471
 
3.3%
Other values (16)33658
10.5%
Lowercase Letter
ValueCountFrequency (%)
n73678
15.7%
a68481
14.6%
o54592
11.6%
s47002
10.0%
e42537
9.0%
c35610
7.6%
k35239
7.5%
h23502
 
5.0%
w22294
 
4.7%
i16667
 
3.5%
Other values (15)50618
10.8%
Decimal Number
ValueCountFrequency (%)
228
49.1%
011
 
19.3%
35
 
8.8%
53
 
5.3%
93
 
5.3%
83
 
5.3%
73
 
5.3%
11
 
1.8%
Other Punctuation
ValueCountFrequency (%)
.33211
83.3%
,4876
 
12.2%
&1729
 
4.3%
/29
 
0.1%
@15
 
< 0.1%
%1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
122149
100.0%
Math Symbol
ValueCountFrequency (%)
+16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%
Open Punctuation
ValueCountFrequency (%)
[1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin789511
83.0%
Common162092
 
17.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n73678
 
9.3%
a68481
 
8.7%
C57938
 
7.3%
o54592
 
6.9%
s47002
 
6.0%
L46723
 
5.9%
e42537
 
5.4%
S39039
 
4.9%
c35610
 
4.5%
k35239
 
4.5%
Other values (41)288672
36.6%
Common
ValueCountFrequency (%)
122149
75.4%
.33211
 
20.5%
,4876
 
3.0%
&1729
 
1.1%
/29
 
< 0.1%
228
 
< 0.1%
+16
 
< 0.1%
@15
 
< 0.1%
011
 
< 0.1%
-8
 
< 0.1%
Other values (8)20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII951603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122149
 
12.8%
n73678
 
7.7%
a68481
 
7.2%
C57938
 
6.1%
o54592
 
5.7%
s47002
 
4.9%
L46723
 
4.9%
e42537
 
4.5%
S39039
 
4.1%
c35610
 
3.7%
Other values (59)363854
38.2%

INSPECTION_PERFORMED
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49822 
False
 
25
ValueCountFrequency (%)
True49822
99.9%
False25
 
0.1%
2024-05-07T15:21:50.728926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

INSPECTION_DATE
Categorical

HIGH CARDINALITY

Distinct3116
Distinct (%)6.3%
Missing22
Missing (%)< 0.1%
Memory size3.2 MiB
12/30/2015
 
91
06/27/2020
 
81
06/11/2020
 
80
06/20/2020
 
79
10/28/2020
 
74
Other values (3111)
49420 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters498250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)0.4%

Sample

1st row08/08/2015
2nd row10/12/2021
3rd row12/19/2023
4th row01/13/2015
5th row07/18/2015

Common Values

ValueCountFrequency (%)
12/30/201591
 
0.2%
06/27/202081
 
0.2%
06/11/202080
 
0.2%
06/20/202079
 
0.2%
10/28/202074
 
0.1%
06/09/202074
 
0.1%
12/27/201874
 
0.1%
06/13/202073
 
0.1%
12/19/201572
 
0.1%
12/05/202071
 
0.1%
Other values (3106)49056
98.4%

Length

2024-05-07T15:21:50.813204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12/30/201591
 
0.2%
06/27/202081
 
0.2%
06/11/202080
 
0.2%
06/20/202079
 
0.2%
10/28/202074
 
0.1%
06/09/202074
 
0.1%
12/27/201874
 
0.1%
06/13/202073
 
0.1%
12/19/201572
 
0.1%
12/05/202071
 
0.1%
Other values (3106)49056
98.5%

Most occurring characters

ValueCountFrequency (%)
0112587
22.6%
2109520
22.0%
/99650
20.0%
179418
15.9%
815947
 
3.2%
915604
 
3.1%
315574
 
3.1%
614906
 
3.0%
714461
 
2.9%
513195
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number398600
80.0%
Other Punctuation99650
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112587
28.2%
2109520
27.5%
179418
19.9%
815947
 
4.0%
915604
 
3.9%
315574
 
3.9%
614906
 
3.7%
714461
 
3.6%
513195
 
3.3%
47388
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/99650
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common498250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112587
22.6%
2109520
22.0%
/99650
20.0%
179418
15.9%
815947
 
3.2%
915604
 
3.1%
315574
 
3.1%
614906
 
3.0%
714461
 
2.9%
513195
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII498250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112587
22.6%
2109520
22.0%
/99650
20.0%
179418
15.9%
815947
 
3.2%
915604
 
3.1%
315574
 
3.1%
614906
 
3.0%
714461
 
2.9%
513195
 
2.6%

GI_REQ_INTERNAL_STRUCTURE
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49755 
False
 
92
ValueCountFrequency (%)
True49755
99.8%
False92
 
0.2%
2024-05-07T15:21:50.891250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GI_RESULT_INTERNAL_STRUCTURE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing94
Missing (%)0.2%
Memory size2.8 MiB
N
49601 
A
 
152

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49753
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49601
99.5%
A152
 
0.3%
(Missing)94
 
0.2%

Length

2024-05-07T15:21:50.956023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:51.046836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49601
99.7%
a152
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N49601
99.7%
A152
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49753
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49601
99.7%
A152
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin49753
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49601
99.7%
A152
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII49753
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49601
99.7%
A152
 
0.3%

GI_REQ_EXTERNAL_STRUCTURE
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49791 
False
 
56
ValueCountFrequency (%)
True49791
99.9%
False56
 
0.1%
2024-05-07T15:21:51.141124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GI_RESULT_EXTERNAL_STRUCTURE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing58
Missing (%)0.1%
Memory size2.8 MiB
N
49661 
A
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49789
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49661
99.6%
A128
 
0.3%
(Missing)58
 
0.1%

Length

2024-05-07T15:21:51.210889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:51.288735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49661
99.7%
a128
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N49661
99.7%
A128
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49789
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49661
99.7%
A128
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin49789
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49661
99.7%
A128
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII49789
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49661
99.7%
A128
 
0.3%

GI_REQ_OVERFLOW_PIPES
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49790 
False
 
57
ValueCountFrequency (%)
True49790
99.9%
False57
 
0.1%
2024-05-07T15:21:51.684053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GI_RESULT_OVERFLOW_PIPES
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing59
Missing (%)0.1%
Memory size2.8 MiB
N
49707 
A
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49788
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49707
99.7%
A81
 
0.2%
(Missing)59
 
0.1%

Length

2024-05-07T15:21:51.781559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:51.895165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49707
99.8%
a81
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N49707
99.8%
A81
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49788
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49707
99.8%
A81
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin49788
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49707
99.8%
A81
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII49788
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49707
99.8%
A81
 
0.2%

GI_REQ_ACCESS_LADDERS
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49743 
False
 
104
ValueCountFrequency (%)
True49743
99.8%
False104
 
0.2%
2024-05-07T15:21:51.988078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GI_RESULT_ACCESS_LADDERS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing106
Missing (%)0.2%
Memory size2.8 MiB
N
49597 
A
 
144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49741
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49597
99.5%
A144
 
0.3%
(Missing)106
 
0.2%

Length

2024-05-07T15:21:52.106101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:52.249784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49597
99.7%
a144
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N49597
99.7%
A144
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49741
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49597
99.7%
A144
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin49741
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49597
99.7%
A144
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII49741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49597
99.7%
A144
 
0.3%

GI_REQ_AIR_VENTS
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49795 
False
 
52
ValueCountFrequency (%)
True49795
99.9%
False52
 
0.1%
2024-05-07T15:21:52.326758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GI_RESULT_AIR_VENTS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing54
Missing (%)0.1%
Memory size2.8 MiB
N
49670 
A
 
123

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49793
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49670
99.6%
A123
 
0.2%
(Missing)54
 
0.1%

Length

2024-05-07T15:21:52.428033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:52.568181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49670
99.8%
a123
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N49670
99.8%
A123
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49793
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49670
99.8%
A123
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin49793
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49670
99.8%
A123
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII49793
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49670
99.8%
A123
 
0.2%

GI_REQ_ROOF_ACCESS
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49723 
False
 
124
ValueCountFrequency (%)
True49723
99.8%
False124
 
0.2%
2024-05-07T15:21:52.633304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

GI_RESULT_ROOF_ACCESS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing126
Missing (%)0.3%
Memory size2.8 MiB
N
49579 
A
 
142

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49721
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49579
99.5%
A142
 
0.3%
(Missing)126
 
0.3%

Length

2024-05-07T15:21:52.731031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:52.831268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49579
99.7%
a142
 
0.3%

Most occurring characters

ValueCountFrequency (%)
N49579
99.7%
A142
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49721
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49579
99.7%
A142
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin49721
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49579
99.7%
A142
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII49721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49579
99.7%
A142
 
0.3%

SI_REQ_SEDIMENT
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49748 
False
 
99
ValueCountFrequency (%)
True49748
99.8%
False99
 
0.2%
2024-05-07T15:21:52.938930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SI_RESULT_SEDIMENT
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing99
Missing (%)0.2%
Memory size2.8 MiB
N
48798 
A
 
950

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N48798
97.9%
A950
 
1.9%
(Missing)99
 
0.2%

Length

2024-05-07T15:21:53.054896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:53.177697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n48798
98.1%
a950
 
1.9%

Most occurring characters

ValueCountFrequency (%)
N48798
98.1%
A950
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49748
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N48798
98.1%
A950
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin49748
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N48798
98.1%
A950
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII49748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N48798
98.1%
A950
 
1.9%

SI_REQ_BIOLOGICAL_GROWTH
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49747 
False
 
100
ValueCountFrequency (%)
True49747
99.8%
False100
 
0.2%
2024-05-07T15:21:53.282482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SI_RESULT_BIOLOGICAL_GROWTH
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing100
Missing (%)0.2%
Memory size2.8 MiB
N
49658 
A
 
89

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49747
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49658
99.6%
A89
 
0.2%
(Missing)100
 
0.2%

Length

2024-05-07T15:21:53.385603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:53.491178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49658
99.8%
a89
 
0.2%

Most occurring characters

ValueCountFrequency (%)
N49658
99.8%
A89
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49747
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49658
99.8%
A89
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin49747
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49658
99.8%
A89
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII49747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49658
99.8%
A89
 
0.2%

SI_REQ_DEBRIS_INSECTS
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49746 
False
 
101
ValueCountFrequency (%)
True49746
99.8%
False101
 
0.2%
2024-05-07T15:21:53.557000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SI_RESULT_DEBRIS_INSECTS
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing101
Missing (%)0.2%
Memory size2.8 MiB
N
49533 
A
 
213

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49746
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49533
99.4%
A213
 
0.4%
(Missing)101
 
0.2%

Length

2024-05-07T15:21:53.623642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:53.743339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49533
99.6%
a213
 
0.4%

Most occurring characters

ValueCountFrequency (%)
N49533
99.6%
A213
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49746
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49533
99.6%
A213
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin49746
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49533
99.6%
A213
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII49746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49533
99.6%
A213
 
0.4%

SI_REQ_RODENT_BIRD
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
True
49785 
False
 
62
ValueCountFrequency (%)
True49785
99.9%
False62
 
0.1%
2024-05-07T15:21:53.811032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

SI_RESULT_RODENT_BIRD
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing62
Missing (%)0.1%
Memory size2.8 MiB
N
49743 
A
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49785
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N49743
99.8%
A42
 
0.1%
(Missing)62
 
0.1%

Length

2024-05-07T15:21:53.887601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:53.993129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
n49743
99.9%
a42
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N49743
99.9%
A42
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49785
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N49743
99.9%
A42
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin49785
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N49743
99.9%
A42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII49785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N49743
99.9%
A42
 
0.1%

SAMPLE_COLLECTED
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing6
Missing (%)< 0.1%
Memory size97.5 KiB
True
49734 
False
 
107
(Missing)
 
6
ValueCountFrequency (%)
True49734
99.8%
False107
 
0.2%
(Missing)6
 
< 0.1%
2024-05-07T15:21:54.065570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

LAB_NAME
Categorical

HIGH CARDINALITY

Distinct738
Distinct (%)1.5%
Missing116
Missing (%)0.2%
Memory size3.5 MiB
EMSL
16737 
ENVIRONMENTAL BUILDING SOLUTIONS LLC
7934 
EMSL Analytical
4309 
Environmental Building Solutions LLC
3586 
EBS
3061 
Other values (733)
14104 

Length

Max length94
Median length93
Mean length17.13478514
Min length1

Characters and Unicode

Total characters852130
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique385 ?
Unique (%)0.8%

Sample

1st rowENVIRONMENTAL BUILDING SOLUTIONS LLC
2nd rowEMSL
3rd rowAtlas Enviornmental lab corp.
4th rowENVIRONMENTAL BUILDING SOLUTIONS LLC
5th rowESML

Common Values

ValueCountFrequency (%)
EMSL16737
33.6%
ENVIRONMENTAL BUILDING SOLUTIONS LLC7934
15.9%
EMSL Analytical4309
 
8.6%
Environmental Building Solutions LLC3586
 
7.2%
EBS3061
 
6.1%
ESML1905
 
3.8%
ENVIRONMENTAL BUILDING SOLUTIONS, LLC1243
 
2.5%
ebs1232
 
2.5%
Environmental Building Solutions, LLC973
 
2.0%
Environmental Building636
 
1.3%
Other values (728)8115
16.3%

Length

2024-05-07T15:21:54.223165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
emsl21600
19.4%
environmental17394
15.6%
building16185
14.5%
solutions15511
13.9%
llc14725
13.2%
analytical4874
 
4.4%
ebs4301
 
3.9%
atlas2012
 
1.8%
esml1908
 
1.7%
lab1643
 
1.5%
Other values (347)11458
10.3%

Most occurring characters

ValueCountFrequency (%)
L84486
 
9.9%
61896
 
7.3%
E55317
 
6.5%
S52118
 
6.1%
N50379
 
5.9%
n44880
 
5.3%
I40553
 
4.8%
i36529
 
4.3%
l34929
 
4.1%
M34367
 
4.0%
Other values (63)356676
41.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter484408
56.8%
Lowercase Letter300116
35.2%
Space Separator61896
 
7.3%
Other Punctuation4462
 
0.5%
Decimal Number1225
 
0.1%
Open Punctuation8
 
< 0.1%
Close Punctuation8
 
< 0.1%
Dash Punctuation7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L84486
17.4%
E55317
11.4%
S52118
10.8%
N50379
10.4%
I40553
8.4%
M34367
7.1%
O29706
 
6.1%
T21013
 
4.3%
B19611
 
4.0%
A19443
 
4.0%
Other values (16)77415
16.0%
Lowercase Letter
ValueCountFrequency (%)
n44880
15.0%
i36529
12.2%
l34929
11.6%
o25298
8.4%
t24954
8.3%
a24564
8.2%
e15972
 
5.3%
r13972
 
4.7%
u13946
 
4.6%
s10810
 
3.6%
Other values (15)54262
18.1%
Decimal Number
ValueCountFrequency (%)
1609
49.7%
8231
 
18.9%
7190
 
15.5%
092
 
7.5%
645
 
3.7%
517
 
1.4%
417
 
1.4%
912
 
1.0%
210
 
0.8%
32
 
0.2%
Other Punctuation
ValueCountFrequency (%)
,3016
67.6%
.1192
 
26.7%
#139
 
3.1%
/81
 
1.8%
'15
 
0.3%
;10
 
0.2%
:6
 
0.1%
&3
 
0.1%
Space Separator
ValueCountFrequency (%)
61896
100.0%
Open Punctuation
ValueCountFrequency (%)
(8
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin784524
92.1%
Common67606
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
L84486
 
10.8%
E55317
 
7.1%
S52118
 
6.6%
N50379
 
6.4%
n44880
 
5.7%
I40553
 
5.2%
i36529
 
4.7%
l34929
 
4.5%
M34367
 
4.4%
O29706
 
3.8%
Other values (41)321260
40.9%
Common
ValueCountFrequency (%)
61896
91.6%
,3016
 
4.5%
.1192
 
1.8%
1609
 
0.9%
8231
 
0.3%
7190
 
0.3%
#139
 
0.2%
092
 
0.1%
/81
 
0.1%
645
 
0.1%
Other values (12)115
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII852130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L84486
 
9.9%
61896
 
7.3%
E55317
 
6.5%
S52118
 
6.1%
N50379
 
5.9%
n44880
 
5.3%
I40553
 
4.8%
i36529
 
4.3%
l34929
 
4.1%
M34367
 
4.0%
Other values (63)356676
41.9%

NYS_CERTIFIED
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing114
Missing (%)0.2%
Memory size2.8 MiB
Y
49630 
D
 
85
N
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49733
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y49630
99.6%
D85
 
0.2%
N18
 
< 0.1%
(Missing)114
 
0.2%

Length

2024-05-07T15:21:54.359392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:54.444500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
y49630
99.8%
d85
 
0.2%
n18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Y49630
99.8%
D85
 
0.2%
N18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49733
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y49630
99.8%
D85
 
0.2%
N18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin49733
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y49630
99.8%
D85
 
0.2%
N18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII49733
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y49630
99.8%
D85
 
0.2%
N18
 
< 0.1%

ANALYTES
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing109
Missing (%)0.2%
Memory size2.8 MiB
B
49738 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49738
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B49738
99.8%
(Missing)109
 
0.2%

Length

2024-05-07T15:21:54.521005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:54.634535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
b49738
100.0%

Most occurring characters

ValueCountFrequency (%)
B49738
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49738
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B49738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin49738
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B49738
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII49738
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B49738
100.0%

COLIFORM
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing135
Missing (%)0.3%
Memory size2.8 MiB
A
49697 
P
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49712
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A49697
99.7%
P15
 
< 0.1%
(Missing)135
 
0.3%

Length

2024-05-07T15:21:54.698183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:54.790992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a49697
> 99.9%
p15
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A49697
> 99.9%
P15
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49712
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A49697
> 99.9%
P15
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin49712
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A49697
> 99.9%
P15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII49712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A49697
> 99.9%
P15
 
< 0.1%

ECOLI
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing135
Missing (%)0.3%
Memory size2.8 MiB
A
49694 
P
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters49712
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A49694
99.7%
P18
 
< 0.1%
(Missing)135
 
0.3%

Length

2024-05-07T15:21:54.858404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2024-05-07T15:21:54.997211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
a49694
> 99.9%
p18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A49694
> 99.9%
P18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter49712
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A49694
> 99.9%
P18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin49712
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A49694
> 99.9%
P18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII49712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A49694
> 99.9%
P18
 
< 0.1%

MEET_STANDARDS
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing38723
Missing (%)77.7%
Memory size97.5 KiB
True
11113 
False
 
11
(Missing)
38723 
ValueCountFrequency (%)
True11113
 
22.3%
False11
 
< 0.1%
(Missing)38723
77.7%
2024-05-07T15:21:55.093753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

DELETED
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size48.8 KiB
False
49796 
True
 
51
ValueCountFrequency (%)
False49796
99.9%
True51
 
0.1%
2024-05-07T15:21:55.201608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

LATITUDE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5524
Distinct (%)11.1%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean40.75580716
Minimum40.514811
Maximum40.908632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:55.308047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40.514811
5-th percentile40.695426
Q140.738851
median40.756105
Q340.77524
95-th percentile40.823477
Maximum40.908632
Range0.393821
Interquartile range (IQR)0.036389

Descriptive statistics

Standard deviation0.04147346128
Coefficient of variation (CV)0.001017608635
Kurtosis4.164851459
Mean40.75580716
Median Absolute Deviation (MAD)0.018157
Skewness-0.4370002353
Sum2027682.918
Variance0.00172004799
MonotonicityNot monotonic
2024-05-07T15:21:55.452353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.764149115
 
0.2%
40.703608100
 
0.2%
40.75700490
 
0.2%
40.76824480
 
0.2%
40.71300179
 
0.2%
40.74827671
 
0.1%
40.71053670
 
0.1%
40.70734769
 
0.1%
40.6557659
 
0.1%
40.75416259
 
0.1%
Other values (5514)48960
98.2%
(Missing)95
 
0.2%
ValueCountFrequency (%)
40.51481128
0.1%
40.5721697
 
< 0.1%
40.5727656
 
< 0.1%
40.5728339
 
< 0.1%
40.5736218
 
< 0.1%
40.57369820
< 0.1%
40.5738386
 
< 0.1%
40.574489
 
< 0.1%
40.5747927
 
< 0.1%
40.5748422
 
< 0.1%
ValueCountFrequency (%)
40.9086329
< 0.1%
40.9078289
< 0.1%
40.9066569
< 0.1%
40.9055991
 
< 0.1%
40.9048945
< 0.1%
40.9047562
 
< 0.1%
40.9046664
< 0.1%
40.9032455
< 0.1%
40.9003219
< 0.1%
40.8983333
 
< 0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION

Distinct5456
Distinct (%)11.0%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean-73.96932981
Minimum-74.236572
Maximum-73.713493
Zeros0
Zeros (%)0.0%
Negative49752
Negative (%)99.8%
Memory size389.6 KiB
2024-05-07T15:21:55.634000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-74.236572
5-th percentile-74.008661
Q1-73.98934
median-73.97791
Q3-73.9618285
95-th percentile-73.88164
Maximum-73.713493
Range0.523079
Interquartile range (IQR)0.0275115

Descriptive statistics

Standard deviation0.03852047902
Coefficient of variation (CV)-0.0005207628503
Kurtosis9.607390671
Mean-73.96932981
Median Absolute Deviation (MAD)0.013229
Skewness2.265310183
Sum-3680122.097
Variance0.001483827304
MonotonicityNot monotonic
2024-05-07T15:21:55.745170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.955251124
 
0.2%
-74.009691100
 
0.2%
-74.01099792
 
0.2%
-73.98252690
 
0.2%
-73.98233980
 
0.2%
-74.00418179
 
0.2%
-74.00937878
 
0.2%
-73.9846971
 
0.1%
-73.98396265
 
0.1%
-73.99923561
 
0.1%
Other values (5446)48912
98.1%
(Missing)95
 
0.2%
ValueCountFrequency (%)
-74.23657228
0.1%
-74.1712566
 
< 0.1%
-74.1712446
 
< 0.1%
-74.0924929
 
< 0.1%
-74.0807297
 
< 0.1%
-74.0765848
 
< 0.1%
-74.07326110
 
< 0.1%
-74.064482
 
< 0.1%
-74.0406488
 
< 0.1%
-74.0373778
 
< 0.1%
ValueCountFrequency (%)
-73.71349348
0.1%
-73.7136272
 
< 0.1%
-73.7421875
 
< 0.1%
-73.751217
 
< 0.1%
-73.7512352
 
< 0.1%
-73.7531796
 
< 0.1%
-73.7532049
 
< 0.1%
-73.7534548
 
< 0.1%
-73.7592839
 
< 0.1%
-73.769038
 
< 0.1%

COMMUNITY_BOARD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.771124779
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:55.854085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile11
Maximum81
Range80
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.17385907
Coefficient of variation (CV)0.5499550247
Kurtosis131.7677664
Mean5.771124779
Median Absolute Deviation (MAD)2
Skewness5.937725023
Sum287125
Variance10.0733814
MonotonicityNot monotonic
2024-05-07T15:21:55.967161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
512069
24.2%
88255
16.6%
66034
12.1%
75857
11.7%
14297
 
8.6%
23950
 
7.9%
43316
 
6.7%
91541
 
3.1%
31115
 
2.2%
111006
 
2.0%
Other values (10)2312
 
4.6%
ValueCountFrequency (%)
14297
 
8.6%
23950
 
7.9%
31115
 
2.2%
43316
 
6.7%
512069
24.2%
66034
12.1%
75857
11.7%
88255
16.6%
91541
 
3.1%
10621
 
1.2%
ValueCountFrequency (%)
8120
 
< 0.1%
555
 
< 0.1%
188
 
< 0.1%
1728
 
0.1%
16171
 
0.3%
1533
 
0.1%
14162
 
0.3%
13404
0.8%
12860
1.7%
111006
2.0%

COUNCIL_DISTRICT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49
Distinct (%)0.1%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.222965911
Minimum1
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:56.087489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile33
Maximum51
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.276344355
Coefficient of variation (CV)1.284284665
Kurtosis6.228750238
Mean7.222965911
Median Absolute Deviation (MAD)1
Skewness2.625757536
Sum359357
Variance86.05056459
MonotonicityNot monotonic
2024-05-07T15:21:56.269678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
414895
29.9%
37215
14.5%
14968
 
10.0%
64927
 
9.9%
24106
 
8.2%
53541
 
7.1%
71314
 
2.6%
331126
 
2.3%
8641
 
1.3%
9615
 
1.2%
Other values (39)6404
12.8%
ValueCountFrequency (%)
14968
 
10.0%
24106
 
8.2%
37215
14.5%
414895
29.9%
53541
 
7.1%
64927
 
9.9%
71314
 
2.6%
8641
 
1.3%
9615
 
1.2%
10446
 
0.9%
ValueCountFrequency (%)
5128
 
0.1%
4948
 
0.1%
48178
0.4%
47190
0.4%
4522
 
< 0.1%
4433
 
0.1%
4377
0.2%
42129
0.3%
41147
0.3%
40120
0.2%

CENSUS_TRACT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct516
Distinct (%)1.0%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3248.012341
Minimum1
Maximum155101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:56.381779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q178
median122
Q3195
95-th percentile16001
Maximum155101
Range155100
Interquartile range (IQR)117

Descriptive statistics

Standard deviation11467.20125
Coefficient of variation (CV)3.530528843
Kurtosis57.31772678
Mean3248.012341
Median Absolute Deviation (MAD)53
Skewness6.573333742
Sum161595110
Variance131496704.6
MonotonicityNot monotonic
2024-05-07T15:21:56.506089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9812
 
1.6%
96734
 
1.5%
113695
 
1.4%
137691
 
1.4%
82644
 
1.3%
13609
 
1.2%
7605
 
1.2%
102592
 
1.2%
109573
 
1.1%
74565
 
1.1%
Other values (506)43232
86.7%
ValueCountFrequency (%)
1182
 
0.4%
318
 
< 0.1%
651
 
0.1%
7605
1.2%
8108
 
0.2%
9812
1.6%
11153
 
0.3%
1248
 
0.1%
13609
1.2%
15145
 
0.3%
ValueCountFrequency (%)
15510150
0.1%
1227027
 
< 0.1%
12270117
 
< 0.1%
1032022
 
< 0.1%
1010025
 
< 0.1%
10100113
 
< 0.1%
9980217
 
< 0.1%
9970422
< 0.1%
9970310
 
< 0.1%
9720332
0.1%

BBL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5235
Distinct (%)10.5%
Missing122
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1300742793
Minimum0
Maximum5080340045
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size389.6 KiB
2024-05-07T15:21:56.642426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000800004
Q11008410018
median1012600001
Q31015180001
95-th percentile3061207501
Maximum5080340045
Range5080340045
Interquartile range (IQR)6769983

Descriptive statistics

Standard deviation766522392.2
Coefficient of variation (CV)0.5892958978
Kurtosis5.862141005
Mean1300742793
Median Absolute Deviation (MAD)3889933
Skewness2.636551971
Sum6.467943539 × 1013
Variance5.875565777 × 1017
MonotonicityNot monotonic
2024-05-07T15:21:56.811048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1014800001124
 
0.2%
1000327501100
 
0.2%
100058000199
 
0.2%
101261750190
 
0.2%
100721000789
 
0.2%
101604000689
 
0.2%
101049750180
 
0.2%
100121000179
 
0.2%
100253000177
 
0.2%
100835004171
 
0.1%
Other values (5225)48827
98.0%
(Missing)122
 
0.2%
ValueCountFrequency (%)
03
 
< 0.1%
100004750132
0.1%
100005001016
< 0.1%
100005750122
< 0.1%
10000875014
 
< 0.1%
10000900019
 
< 0.1%
100009001419
< 0.1%
100009002930
0.1%
10001000149
 
< 0.1%
100010001621
< 0.1%
ValueCountFrequency (%)
508034004528
0.1%
50282975012
 
< 0.1%
501272001112
< 0.1%
50059000019
 
< 0.1%
50001300087
 
< 0.1%
50000200158
 
< 0.1%
500001750110
 
< 0.1%
41617700559
 
< 0.1%
41617700358
 
< 0.1%
41617700019
 
< 0.1%

NTA
Categorical

HIGH CARDINALITY

Distinct147
Distinct (%)0.3%
Missing95
Missing (%)0.2%
Memory size3.8 MiB
Midtown-Midtown South
8275 
Upper East Side-Carnegie Hill
4392 
Hudson Yards-Chelsea-Flatiron-Union Square
3840 
Turtle Bay-East Midtown
3550 
Upper West Side
3546 
Other values (142)
26149 

Length

Max length53
Median length42
Mean length22.87288953
Min length6

Characters and Unicode

Total characters1137972
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTurtle Bay-East Midtown
2nd rowUpper East Side-Carnegie Hill
3rd rowEast Concourse-Concourse Village
4th rowWest Village
5th rowHunters Point-Sunnyside-West Maspeth

Common Values

ValueCountFrequency (%)
Midtown-Midtown South8275
16.6%
Upper East Side-Carnegie Hill4392
 
8.8%
Hudson Yards-Chelsea-Flatiron-Union Square3840
 
7.7%
Turtle Bay-East Midtown3550
 
7.1%
Upper West Side3546
 
7.1%
Battery Park City-Lower Manhattan2747
 
5.5%
Murray Hill-Kips Bay2270
 
4.6%
Lenox Hill-Roosevelt Island1857
 
3.7%
Lincoln Square1820
 
3.7%
West Village1730
 
3.5%
Other values (137)15725
31.5%

Length

2024-05-07T15:21:56.959096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south9288
 
7.1%
midtown-midtown8275
 
6.3%
upper7938
 
6.1%
east6118
 
4.7%
square5701
 
4.4%
hill5580
 
4.3%
west5531
 
4.2%
side-carnegie4392
 
3.4%
side3882
 
3.0%
hudson3840
 
2.9%
Other values (198)70297
53.7%

Most occurring characters

ValueCountFrequency (%)
t86130
 
7.6%
e85913
 
7.5%
81090
 
7.1%
i79095
 
7.0%
o71818
 
6.3%
a71225
 
6.3%
n70588
 
6.2%
r61925
 
5.4%
l57316
 
5.0%
-45437
 
4.0%
Other values (45)427435
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter829339
72.9%
Uppercase Letter182029
 
16.0%
Space Separator81090
 
7.1%
Dash Punctuation45437
 
4.0%
Other Punctuation69
 
< 0.1%
Open Punctuation4
 
< 0.1%
Close Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t86130
10.4%
e85913
10.4%
i79095
9.5%
o71818
8.7%
a71225
8.6%
n70588
8.5%
r61925
 
7.5%
l57316
 
6.9%
s43032
 
5.2%
d42315
 
5.1%
Other values (15)159982
19.3%
Uppercase Letter
ValueCountFrequency (%)
M27699
15.2%
S26155
14.4%
H20604
11.3%
C19287
10.6%
B13180
7.2%
U12377
 
6.8%
E9860
 
5.4%
L8149
 
4.5%
W6690
 
3.7%
T5201
 
2.9%
Other values (14)32827
18.0%
Other Punctuation
ValueCountFrequency (%)
.57
82.6%
'12
 
17.4%
Space Separator
ValueCountFrequency (%)
81090
100.0%
Dash Punctuation
ValueCountFrequency (%)
-45437
100.0%
Open Punctuation
ValueCountFrequency (%)
(4
100.0%
Close Punctuation
ValueCountFrequency (%)
)4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1011368
88.9%
Common126604
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t86130
 
8.5%
e85913
 
8.5%
i79095
 
7.8%
o71818
 
7.1%
a71225
 
7.0%
n70588
 
7.0%
r61925
 
6.1%
l57316
 
5.7%
s43032
 
4.3%
d42315
 
4.2%
Other values (39)342011
33.8%
Common
ValueCountFrequency (%)
81090
64.1%
-45437
35.9%
.57
 
< 0.1%
'12
 
< 0.1%
(4
 
< 0.1%
)4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1137972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t86130
 
7.6%
e85913
 
7.5%
81090
 
7.1%
i79095
 
7.0%
o71818
 
6.3%
a71225
 
6.3%
n70588
 
6.2%
r61925
 
5.4%
l57316
 
5.0%
-45437
 
4.0%
Other values (45)427435
37.6%

BATCH_DATE
Categorical

HIGH CORRELATION

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
04/19/2024 12:15:07 PM
 
1638
04/19/2024 12:15:11 PM
 
1512
04/19/2024 12:15:22 PM
 
1512
04/19/2024 12:15:39 PM
 
1510
04/19/2024 12:15:17 PM
 
1509
Other values (29)
42166 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1096634
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row04/19/2024 12:15:09 PM
2nd row04/19/2024 12:15:37 PM
3rd row04/19/2024 12:15:40 PM
4th row04/19/2024 12:15:09 PM
5th row04/19/2024 12:15:07 PM

Common Values

ValueCountFrequency (%)
04/19/2024 12:15:07 PM1638
 
3.3%
04/19/2024 12:15:11 PM1512
 
3.0%
04/19/2024 12:15:22 PM1512
 
3.0%
04/19/2024 12:15:39 PM1510
 
3.0%
04/19/2024 12:15:17 PM1509
 
3.0%
04/19/2024 12:15:24 PM1509
 
3.0%
04/19/2024 12:15:35 PM1509
 
3.0%
04/19/2024 12:15:21 PM1507
 
3.0%
04/19/2024 12:15:31 PM1507
 
3.0%
04/19/2024 12:15:32 PM1505
 
3.0%
Other values (24)34629
69.5%

Length

2024-05-07T15:21:57.050219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
04/19/202449847
33.3%
pm49847
33.3%
12:15:071638
 
1.1%
12:15:111512
 
1.0%
12:15:221512
 
1.0%
12:15:391510
 
1.0%
12:15:171509
 
1.0%
12:15:241509
 
1.0%
12:15:351509
 
1.0%
12:15:311507
 
1.0%
Other values (26)37641
25.2%

Most occurring characters

ValueCountFrequency (%)
1168904
15.4%
2168821
15.4%
0109421
10.0%
4104909
9.6%
/99694
9.1%
99694
9.1%
:99694
9.1%
955815
 
5.1%
554359
 
5.0%
P49847
 
4.5%
Other values (5)85476
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number697858
63.6%
Other Punctuation199388
 
18.2%
Space Separator99694
 
9.1%
Uppercase Letter99694
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1168904
24.2%
2168821
24.2%
0109421
15.7%
4104909
15.0%
955815
 
8.0%
554359
 
7.8%
319128
 
2.7%
76052
 
0.9%
85999
 
0.9%
64450
 
0.6%
Other Punctuation
ValueCountFrequency (%)
/99694
50.0%
:99694
50.0%
Uppercase Letter
ValueCountFrequency (%)
P49847
50.0%
M49847
50.0%
Space Separator
ValueCountFrequency (%)
99694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common996940
90.9%
Latin99694
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1168904
16.9%
2168821
16.9%
0109421
11.0%
4104909
10.5%
/99694
10.0%
99694
10.0%
:99694
10.0%
955815
 
5.6%
554359
 
5.5%
319128
 
1.9%
Other values (3)16501
 
1.7%
Latin
ValueCountFrequency (%)
P49847
50.0%
M49847
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1096634
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1168904
15.4%
2168821
15.4%
0109421
10.0%
4104909
9.6%
/99694
9.1%
99694
9.1%
:99694
9.1%
955815
 
5.1%
554359
 
5.0%
P49847
 
4.5%
Other values (5)85476
7.8%

Interactions

2024-05-07T15:21:41.348384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:21.477843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:23.305346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.552640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.397433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:29.201390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.891365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.512878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:34.314917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.274445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.892435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.532041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:41.481083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:21.634597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:23.451628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.697974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.529974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:29.326177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.026251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.634907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:34.434781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.427153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.087770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.697992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:41.598995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:21.749191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:23.580043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.814795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.685036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:29.439901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.191579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.798916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:34.618110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.569491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.213223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.843684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:41.705716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:21.947729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:23.701195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.997365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.827828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:29.621801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.348503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.992708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:34.803967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.727983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.331225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.956027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.049206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:22.129580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:24.323210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:26.165854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.956761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:29.773170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.488904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:33.124374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.126368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.834399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.428026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:40.057854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.184865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:22.265011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:24.457034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:26.285569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.101452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:29.901745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.625457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:33.279555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.255826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.935834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.546215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:40.212442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.312021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:22.440439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:24.585250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:26.423014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.241255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.015037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.713697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:33.426838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.359630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.059615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.661441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:40.396728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.500016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:22.565434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:24.783026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:26.655272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.371294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.209641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.861619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:33.559635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.524995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.227237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.841271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:40.538112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.654635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:22.680320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:24.966685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:26.782557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.521298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.319439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:31.999652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:33.702515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.647538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.389770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:38.969325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:40.720218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.803989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:22.863393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.128575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:26.982427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.711294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.457206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.108516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:33.897169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.768960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.518795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.117132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:40.880023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:42.899104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:23.007636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.273649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.152812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.841341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.582433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.265017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:34.031353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:35.902368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.659190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.231418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:41.034024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:43.009209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:23.142934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:25.409621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:27.268145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:28.961030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:30.716322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:32.401427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:34.189484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:36.098899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:37.784456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:39.381253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2024-05-07T15:21:41.185565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2024-05-07T15:21:57.237104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2024-05-07T15:21:57.575038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2024-05-07T15:21:57.725318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2024-05-07T15:21:57.931254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2024-05-07T15:21:58.169648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2024-05-07T15:21:58.530890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2024-05-07T15:21:43.662244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T15:21:45.516763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-07T15:21:46.928810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2024-05-07T15:21:47.823414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

BINBOROUGHZIPHOUSE_NUMSTREET_NAMEBLOCKLOTCONFIRMATION_NUMREPORTING_YEARTANK_NUMINSPECTION_BY_FIRMINSPECTION_PERFORMEDINSPECTION_DATEGI_REQ_INTERNAL_STRUCTUREGI_RESULT_INTERNAL_STRUCTUREGI_REQ_EXTERNAL_STRUCTUREGI_RESULT_EXTERNAL_STRUCTUREGI_REQ_OVERFLOW_PIPESGI_RESULT_OVERFLOW_PIPESGI_REQ_ACCESS_LADDERSGI_RESULT_ACCESS_LADDERSGI_REQ_AIR_VENTSGI_RESULT_AIR_VENTSGI_REQ_ROOF_ACCESSGI_RESULT_ROOF_ACCESSSI_REQ_SEDIMENTSI_RESULT_SEDIMENTSI_REQ_BIOLOGICAL_GROWTHSI_RESULT_BIOLOGICAL_GROWTHSI_REQ_DEBRIS_INSECTSSI_RESULT_DEBRIS_INSECTSSI_REQ_RODENT_BIRDSI_RESULT_RODENT_BIRDSAMPLE_COLLECTEDLAB_NAMENYS_CERTIFIEDANALYTESCOLIFORMECOLIMEET_STANDARDSDELETEDLATITUDELONGITUDECOMMUNITY_BOARDCOUNCIL_DISTRICTCENSUS_TRACTBBLNTABATCH_DATE
01036156.0MANHATTAN10174.0405LEXINGTON AVE129723WTI079413752120152ISSEKS BROS INCY08/08/2015YNYNYNYNYNYNYNYNYNYNYENVIRONMENTAL BUILDING SOLUTIONS LLCYBAANaNNo40.751823-73.9757606.04.092.01.012970e+09Turtle Bay-East Midtown04/19/2024 12:15:09 PM
11042461.0MANHATTAN10065.0131East 66th Street140120WTI960559863620211Rosenwach Tank Co. LLCY10/12/2021YNYNYNYNYNYNYNYNYNYNYEMSLYBAANaNNo40.766513-73.9647488.04.0120.01.014010e+09Upper East Side-Carnegie Hill04/19/2024 12:15:37 PM
22101266.0BRONX10456.01118grand concourse246239WTI276135546420231DCASY12/19/2023YNYNYNYNYNYNYNYNYNYNYAtlas Enviornmental lab corp.YBAANaNNo40.832232-73.9196554.016.018102.02.024620e+09East Concourse-Concourse Village04/19/2024 12:15:40 PM
31009095.0MANHATTAN10003.028EAST 10 STREET5617502WTI592131369920151ISSEKS BROS INCY01/13/2015YNYNYNYNYNYNYNYNYNYNYENVIRONMENTAL BUILDING SOLUTIONS LLCYBAANaNNo40.732489-73.9932602.02.059.01.005618e+09West Village04/19/2024 12:15:09 PM
44003540.0QUEENS11101.030-3047th Avenue2821WTI530005956820151Rosenwach Tank Co. LLCY07/18/2015YNYNYNYNYNYNYNYNYNYNYESMLYBAANaNNo40.743168-73.9365682.026.0199.04.002820e+09Hunters Point-Sunnyside-West Maspeth04/19/2024 12:15:07 PM
51045818.0MANHATTAN10021.0435EAST 70 STREET146521WTI623804604120184Rosenwach Tank Co. LLCY08/16/2018YNYNYNYNYNYNYNYNYNYNYEMSLYBAAYNo40.766129-73.9560668.05.0124.01.014650e+09Lenox Hill-Roosevelt Island04/19/2024 12:15:23 PM
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81079000.0MANHATTAN10013.0100Centre street1671WTI979819187620212DCASY12/05/2021YNYNYNYNYNYNYNYNYNYNNNaNNaNNaNNaNNaNNaNNo40.716084-74.0014251.01.029.01.001670e+09Chinatown04/19/2024 12:15:34 PM
91036079.0MANHATTAN10022.0745Fifth Ave129369WTI407654687120162Isseks Bros. Inc.Y09/17/2016YNYNYNYNYNYNYNYNYNYNYEnvironmental Building Solutions LLCYBAANaNNo40.763326-73.9736955.04.011202.01.012930e+09Midtown-Midtown South04/19/2024 12:15:10 PM

Last rows

BINBOROUGHZIPHOUSE_NUMSTREET_NAMEBLOCKLOTCONFIRMATION_NUMREPORTING_YEARTANK_NUMINSPECTION_BY_FIRMINSPECTION_PERFORMEDINSPECTION_DATEGI_REQ_INTERNAL_STRUCTUREGI_RESULT_INTERNAL_STRUCTUREGI_REQ_EXTERNAL_STRUCTUREGI_RESULT_EXTERNAL_STRUCTUREGI_REQ_OVERFLOW_PIPESGI_RESULT_OVERFLOW_PIPESGI_REQ_ACCESS_LADDERSGI_RESULT_ACCESS_LADDERSGI_REQ_AIR_VENTSGI_RESULT_AIR_VENTSGI_REQ_ROOF_ACCESSGI_RESULT_ROOF_ACCESSSI_REQ_SEDIMENTSI_RESULT_SEDIMENTSI_REQ_BIOLOGICAL_GROWTHSI_RESULT_BIOLOGICAL_GROWTHSI_REQ_DEBRIS_INSECTSSI_RESULT_DEBRIS_INSECTSSI_REQ_RODENT_BIRDSI_RESULT_RODENT_BIRDSAMPLE_COLLECTEDLAB_NAMENYS_CERTIFIEDANALYTESCOLIFORMECOLIMEET_STANDARDSDELETEDLATITUDELONGITUDECOMMUNITY_BOARDCOUNCIL_DISTRICTCENSUS_TRACTBBLNTABATCH_DATE
498371039577.0MANHATTAN10022.09262 AVENUE13427502WTI825642279920151ISSEKS BROS INCY10/07/2015YNYNYNYNYNYNYNYNYNYNYENVIRONMENTAL BUILDING SOLUTIONS LLCYBAANaNNo40.754391-73.9688656.04.098.01.013428e+09Turtle Bay-East Midtown04/19/2024 12:15:11 PM
498381050288.0MANHATTAN10128.0345EAST 93 STREET155623WTI654779003420191Rosenwach Tank Co. LLCY08/13/2019YNYNYNYNYNYNYNYNYNYNYEMSLYBAAYNo40.781501-73.9465458.05.0154.01.015560e+09Yorkville04/19/2024 12:15:19 PM
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498411038755.0MANHATTAN10017.0307EAST 44TH STREET13376WTI605143104120241ISSEKS BROS., INC.Y01/10/2024YNYNYNYNYNYNYNYNYNYNYENVIRONMENTAL BUILDING SOLUTIONS, LLCYBAANaNNo40.750949-73.9708416.04.090.01.013370e+09Turtle Bay-East Midtown04/19/2024 12:15:38 PM
498421046609.0MANHATTAN10028.0993Fifth Avenue14923WTI191230513420161Isseks Bros IncY06/02/2016YNYNYNYNYNYNYNYNYNYNYEnvironmental Building Solutions LLCYBAANaNNo40.777917-73.9630418.04.0142.0NaNUpper East Side-Carnegie Hill04/19/2024 12:15:11 PM
498431015240.0MANHATTAN10018.01359BROADWAY81222WTI820902237020181Rosenwach Tank Co. LLCY03/24/2018YNYNYNYNYNYNYNYNYNYNYEMSLYBAAYNo40.751704-73.9875995.03.0109.01.008120e+09Midtown-Midtown South04/19/2024 12:15:18 PM
498441016080.0MANHATTAN10003.0855 AVENUE8441WTI212128704120191Rosenwach Tank Co. LLCY11/23/2019YNYNYNYNYNYNYNYNYNYNYEMSL AnalyticalYBAAYNo40.737416-73.9925815.02.052.01.008440e+09Hudson Yards-Chelsea-Flatiron-Union Square04/19/2024 12:15:25 PM
498454433878.0QUEENS11375.070-25YELLOWSTONE BOULEVARD323635WTI044212089220171Rosenwach Tank Co. LLCY05/04/2017YNYNYNYNYNYNYNYNYNYNYEMSLYBAAYNo40.722598-73.8498166.029.0711.04.032360e+09Forest Hills04/19/2024 12:15:09 PM
498461040043.0MANHATTAN10022.0339East 58th Street135117WTI172404902720151Rosenwach Tank Co. LLCY04/11/2015YNYNYNYNYNYNYNYNYNYNYESMLYBAANaNNo40.759440-73.9634886.05.0108.01.013510e+09Turtle Bay-East Midtown04/19/2024 12:15:09 PM